Bare-bones particle swarm optimization with disruption operator

Liu, Hao; Ding, Guiyan; Wang, Bing

doi:10.1016/j.amc.2014.03.152

Cited by 30 publications

(15 citation statements)

References 27 publications

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“…In addition, a new centroid-based encoding schema was designed for each particle, and the Chernoff bounds were applied to decide a proper population size. Liu et al [37] proposed a novel disruption strategy, originating from astrophysics, to shift the abilities between exploration and exploitation during the search process, with the aim of enhancing population diversity and speeding up convergence rate of BBPSO. They researched the distribution and diversity on the proposed disruption operator and illustrated the position relationship between the original and disrupted position.…”

Section: Bbpsomentioning

confidence: 99%

“…Due to the page limit, we cannot list all of them. [20], Jamalipour et al [21], Bagheri et al [22], Tang et al [23], Davoodi et al [24], Li and Xiao [25], Yumin and Li [26], Jia et al [27], and Gholizadeh and Moghadas [28] BBPSO Zhang et al [30], Zhang et al [31], Zhang et al [32], Zhang et al [33], Blackwell [34], Wang et al [35], Jiang and Wang [36], Liu et al [37], Campos et al [38], and Zhang et al [39] CPSO Chuang et al [40], Zhang and Wu [41], Dai et al [42], Li et al [43], Wu et al [44], Zhang et al [45], Zhang et al [46], Yang et al [47], Son [48], He et al [49], Zeng and Sun [50], and Pluhacek et al [ [95], and Kalayci and Gupta [96] Other modifications Chuang et al [97], Shi and Liu [98], Zhang et al [99], Liu et al [100], Shen et al [101], Lin et al [102], Wang and Watada [103], Li et al [104], Lu et al [105], Mattos et al [106], Wu et al …”

Section: Trends Of Applicationsmentioning

confidence: 99%

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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

632

354

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Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

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Section: Bbpsomentioning

confidence: 99%

Section: Trends Of Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Zhang

Wang

2015

Mathematical Problems in Engineering

632

354

View full text Add to dashboard Cite

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“…In 2014, Liu et al 26 other solutions can potentially disrupt and scatter in the search space. It is parameter-free and more compact than the BPSO.…”

Section: Solution Methodsmentioning

confidence: 99%

Mitigating the volatility of renewable distributed generators with plug-in electric vehicles in optimal operation of active distribution networks

Zhang

Zhao

et al. 2015

Journal of Renewable and Sustainable Energy

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Active distribution networks (ADNs) are becoming popular worldwide, where renewable distributed generators (DGs) are responsible to support system operation and allowed to be integrated to networks under non-firm connection agreements. With the increasing penetration of renewable DGs, their power volatility cannot be neglected any more. In order to overcome the negative influences caused by the volatility of renewable DGs, a strategy for mitigating the volatility with plug-in electric vehicles (PEVs) is proposed for economic and safe operation of ADNs. First, a PEV aggregator is proposed to control a group of PEVs, which can act as demand response in ADNs by charging/discharging power. Then, a volatility index (VI) is defined to measure the volatile degree of power from renewable DGs in a certain time period. Based on VI, the formulation of union volatility index is derived, considering the power charged/discharged by PEV aggregators. Thereafter, an optimal operation model of ADN operation with volatility constraints is proposed. Its objective is to minimize the operation cost of ADNs. This optimal model is solved by a novel algorithm-disruption bare-bones particle swarm optimization algorithm, which integrates disruption strategies into the bare-bones particle swarm optimization algorithm. The proposed model is demonstrated on a 37-node radial distribution system with a PEV aggregator. Five scenarios are carefully designed to certify the necessity of mitigating the volatility of renewable DGs in network economic operation. It is concluded that mitigating the volatility of renewable DGs and adopting real-time pricing schemes on PEV aggregators are particularly effective for optimal operation of ADNs. V C 2015 AIP Publishing LLC. [http://dx.

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“…On the other hand, there are some researchers who focused on keeping swarm diversity and proposed some measures to enhance the diversity of the swarm, such as separate iteration [26] and dynamic local search strategies [29]. Liu et al [30] utilized a novel disruption strategy, originating from astrophysics, to shift the abilities between exploration and exploitation during the search process, and an opposition-based learning (OBL) modified strategy was further investigated [31].…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Identification Based on l₁Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

Huang

Lei

2019

Mathematical Problems in Engineering

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Structural damage identification (SDI) plays a major role in structural health monitoring (SHM), which has been demanded by researchers to better face the challenges in the aging civil engineering, such as bridge structure and building structure. Many methods have been developed for the application to the real structures, but there are still some difficulties which result in inaccurate, even false damage identification. As a variant of particle swarm optimization (PSO), bare bones particle swarm optimization (BBPSO) is a simple but very powerful optimization tool. However, it is easy to be trapped in the local optimal state like other PSO algorithms, especially in SDI problems. In order to improve its performance in SDI problems, this paper aims to propose a novel optimization algorithm which is named as bare bones particle swarm optimization with double jump (BBPSODJ) for finding a new solution to the SDI problem in SHM field. To begin with, after the introduction of sparse recovery theory, the mathematical model for SDI is established where an objective function based on l1 regularization is constructed. Secondly, according to the basic theory of the BBPSODJ, a double jump strategy based on the BBPSO is designed to enhance the dynamic of particles, and it is able to make a large change in particle searching scopes, which can improve the search behaviour of BBPSO and prevent the algorithm from being trapped into local minimum state. Thirdly, three optimization test functions and a numerical example are utilized to validate the optimization performance of BBPSO, traditional PSO, and genetic algorithm (GA) comparatively; it is obvious that the proposed BBPSODJ shows great self-adapting property and good performance in the optimization process by introducing the novel double jump strategy. Finally, in the laboratory, an experimental example of steel frame with 4 damage cases is implemented to further assess the damage identification capability of the BBPSODJ with l1 regularization. From the damage identification results, it can be seen that the proposed BBPSODJ algorithm, which is efficient and robust, has great potential in the field of SHM.

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Bare-bones particle swarm optimization with disruption operator

Cited by 30 publications

References 27 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Mitigating the volatility of renewable distributed generators with plug-in electric vehicles in optimal operation of active distribution networks

Structural Damage Identification Based on l₁Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

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Bare-bones particle swarm optimization with disruption operator

Cited by 30 publications

References 27 publications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Mitigating the volatility of renewable distributed generators with plug-in electric vehicles in optimal operation of active distribution networks

Structural Damage Identification Based on l1Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy

Contact Info

Product

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Structural Damage Identification Based on l₁Regularization and Bare Bones Particle Swarm Optimization with Double Jump Strategy